Research on the microbiota and metabolic characteristics of type 2 chronic rhinosinusitis based on the analysis of 16S amplicon sequencing combined with metabolomics

Objective:
To investigate the diversity of nasal microbiota and metabolic characteristics of patients with Type 
2 chronic rhinosinusitis, as well as the interactions and potential regulatory relationships between 
the nasal microbiota and their metabolites by conducting a 16S amplicon sequencing and 
metabolomics analysis on nasal secretions from patients with Type 2 chronic rhinosinusitis.
Methods:
28 patients with Type 2 Chronic Rhinosinusitis (T2-CRS) were selected from the Guangzhou Red 
Cross Hospital Affiliated to Jinan University. These patients underwent endoscopic sinus surgery 
and were diagnosed with T2-CRS between July 2023 and October 2024, based on the presence of 
eosinophilic infiltration (Eos10/high-power field) in the nasal polyp pathological tissues after 
surgery. 12 healthy individuals without nasal disease was enrolled as the control group. For each 
participant, two samples of nasal secretions were collected. One sample was processed using 16S 
amplicon sequencing technology on the Agilent 2100 Bioanalyzer platform, targeting the V3-V4 
hypervariable regions of the 16S rDNA to sequence all bacterial taxa present in the sample. The 
sequencing results were subjected to Operational Taxonomic Unit (OTU) clustering, followed by 
bioinformatics analyses (including α-diversity and β-diversity analyses, LEfSe analysis, and 
differential abundance analysis) and statistical processing to characterize and compare the nasal 
microbiota profiles and differences between the T2-CRS group and the control group. The other 
tube of nasal secretion sample was subjected to extraction and analyzed using Ultra-Performance 
Liquid Chromatography coupled with Mass Spectrometry (UPLC-MS). The mass spectrometry 
data were interpreted by integrating information from the BGI Metabolome database, the mzCloud 
database, and the ChemSpider online database. This approach facilitated the identification and 
comparison of the metabolic profiles and differences in nasal microbiota metabolites between the 
T2-CRS group and the control group. Finally, the identified differentially abundant microbiota and metabolites were analyzed through an integrative multi-omics approach. This reveals the 
metabolites closely related to the distribution of microbial communities and the dominant species 
that induce metabolic changes, and explores the intrinsic regulatory pathways of the organism 
involving nasal microbiota and metabolites.
Results:
The sequencing results showed that both T2-CRS patients and healthy control group had a rich 
and diverse bacterial community in the nasal cavity. Compared with the healthy control group, the 
T2-CRS group exhibited a decrease in α-diversity, with statistically significant differences (P < 
0.05) observed in the α-diversity-related indices, including the Chao 1 index, Sobs index, Coverage 
index, and Shannon index. Principal Coordinates Analysis (PCoA) revealed a significant 
difference in β-diversity between the two groups (P < 0.05). At the phylum level, the nasal 
microbiota of the T2-CRS group had higher relative abundances of Fusobacteriota, 
Acidobacteriota, and Campylobacterota, with significant differences (P < 0.05). In contrast, the 
nasal microbiota of the control group had higher relative abundances of Pseudomonadota, 
Actinomycetota, Bacteroidota, and Gemmatimonadota, but these differences were not statistically 
significant (P > 0.05). At the genus level, the nasal microbiota of the T2-CRS group exhibited
higher relative abundances of Haemophilus, Pseudomonas, and Burkholderia, with significant 
differences (P < 0.05). In the control group, the relative abundances of Sphingomonas, 
Bradyrhizobium, Cutibacterium, Methylorubrum, and Lawsonella were higher and showed 
significant differences (P < 0.05). At the species level, Haemophilus species, Pseudomonas 
aeruginosa, and Burkholderia cepacia exhibited higher relative abundances in the T2-CRS group 
with significant differences (P < 0.05). In contrast, the control group had higher relative 
abundances of Sphingomonas azotifigens, Cutibacterium acnes, Methylorubrum extorquens, and 
Lawsonella clevelandensis, with significant differences observed (P < 0.05).
In terms of metabolomics, there were significant differences in the metabolic profiles between the 
T2-CRS group and the control group. A total of 46 differential metabolites were identified using 
fold change (FC), P-value, and variable importance in projection (VIP) values. In the T2-CRS 
group, the upregulated metabolites mainly included 10Z - nonadecenoic acid, 4 -
hydroxyphenylpyruvic acid, pimelic acid, indoleacetic acid, and others, predominantly fatty acids.
In the T2-CRS group, the downregulated metabolites mainly included Leucine, L-aspartic acid, Asparagine, D-ornithine, L-glutathione oxidized etc., predominantly amino acids. These 
differential metabolites were primarily annotated and enriched in metabolic pathways like
tryptophan metabolism, glycerophospholipid metabolism, glutathione metabolism, and arginine 
and proline metabolism. Subsequently, 10 potential biomarkers were selected based on their 
biological significance and the receiver operating characteristic (ROC) curve analysis, including 
10Z - nonadecenoic acid, 4 - hydroxyphenylpyruvic acid, indoleacetic acid etc.
After conducting an integrative multi omics analysis of the differential microbial communities and 
metabolites, it was revealed that certain differential microbes, such as Haemophilus species, 
Staphylococcus aureus, and Streptococcus pneumoniae, were significantly positively correlated 
with specific metabolites, including 10Z - nonadecenoic acid, 4 - hydroxyphenylpyruvic acid, L -
kynurenine, and xanthine (P < 0.05). Differential microbes such as Lawsonella clevelandensis, 
Pseudomonas aeruginosa, and Sphingomonas azotifigens exhibited significant negative 
correlations with metabolites including L(+) - ornithine, guanidoacetic acid, and leucine (P < 0.05).
Conclusions:
This study elucidated the potential pathogenesis of T2-CRS from the perspectives of microbiomics, 
metabolomics, and their integrative analysis, with the aim of providing new directions for the 
diagnosis and targeted treatment of T2-CRS.